Image display method and apparatus based on artificial intelligence, device, and medium

ABSTRACT

An image display method includes: processing a first image to obtain a first feature image, the first image being an image of a local area of a smear captured by a microscope, and the local area including multiple objects to be tested; obtaining a second feature image corresponding to the first feature image, the second feature image and the first feature image having a same size; obtaining a third feature image according to an image obtained by overlaying the first feature image and the second feature image, a feature point in the third feature image indicating a possibility that one of the multiple objects is an abnormal object; obtaining a second image according to the third feature image; and displaying the second image superimposed on the first image.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application is a continuation application of PCT Patent ApplicationNo. PCT/CN2021/080603, entitled “IMAGE DISPLAY METHOD AND APPARATUSBASED ON ARTIFICIAL INTELLIGENCE, AND DEVICE AND MEDIUM” and filed onMar. 12, 2021, which claims priority to Chinese Patent Application No.202010320738.1, entitled “IMAGE DISPLAY METHOD AND APPARATUS BASED ONARTIFICIAL INTELLIGENCE, DEVICE, AND MEDIUM” and filed with the ChinaNational Intellectual Property Administration on Apr. 22, 2020, theentire contents of both of which are incorporated herein by reference.

FIELD OF THE TECHNOLOGY

The present disclosure relates to the field of artificial intelligencetechnologies, and in particular, to an image display method andapparatus based on artificial intelligence, a device, and a medium.

BACKGROUND OF THE DISCLOSURE

Cervical cancer is a malignant tumor disease with a serious threat towomen and a high mortality rate. As screening methods of cervical cancerare developed, a cervical cancer lesion can be successfully found at anearly disease stage through the thin-layer liquid-based cytologytechnique, to reduce an incidence rate and a mortality rate. However, ina screening process, a medical practitioner needs to make a judgment ona cervical liquid-based smear based on personal experience, to determinewhether a lesion is present. As a result, when the medical practitioneris subject to a high workload, the problem of misdiagnosis and misseddiagnosis easily occurs.

Currently, the workload of a medical practitioner can be reduced in acomputer-assisted manner. That is, in a screening process, a computerdevice comprehensively scans a cervical liquid-based smear, segments,through an image processing technology, an image obtained throughscanning, extracts a cell image therein, and determines whether a lesionis present in a cell according to a cell feature.

The foregoing technical solution has the following problem: Costs of thedevice for scanning the smear are high. Smear scanning consumes a verylong time. Besides, because the cell image is complex and nucleuses mayoverlap or may be in contact with each other, the computer device cannotmake an accurate judgment on the image obtained through scanning, andthe medical practitioner still needs to make a judgment again tocomplete the screening. Consequently, this reduces screening efficiencyof doctors.

SUMMARY

Embodiments of the present disclosure provide an image display methodand apparatus based on artificial intelligence, a device, and a medium.The technical solutions are as follows:

According to one aspect, an image display method based on artificialintelligence is provided, executed by a computer device. The methodincludes: processing a first image to obtain a first feature image, thefirst image being an image of a local area of a smear captured by amicroscope, and the local area including multiple objects to be tested;obtaining a second feature image corresponding to the first featureimage, the second feature image and the first feature image having asame size; obtaining a third feature image according to an imageobtained by overlaying the first feature image and the second featureimage, a feature point in the third feature image indicating apossibility that one of the multiple objects is an abnormal object;obtaining a second image according to the third feature image; anddisplaying the second image superimposed on the first image

According to another aspect, an image display apparatus based onartificial intelligence is provided. The apparatus includes: a memoryand a processor coupled to the memory. The processor is configured to:process a first image to obtain a first feature image, the first imagebeing an image of a local area of a smear captured by a microscope, andthe local area including multiple objects to be tested; obtain a secondfeature image corresponding to the first feature image, the secondfeature image and the first feature image having a same size; obtain athird feature image according to an image obtained by overlaying thefirst feature image and the second feature image, a feature point in thethird feature image indicating a possibility that one of the multipleobjects is an abnormal object; obtain a second image according to thethird feature image; and display the second image superimposed on thefirst image. According to another aspect, an image display system basedon artificial intelligence is provided, the system including: anobjective lens, an ocular lens, an image capturing component, an imageprocessing component, and an augmented reality component. The objectivelens is configured to magnify a local area of a smear, the local areaincluding multiple objects to be tested. The image capturing componentis connected to the objective lens and configured to capture a firstimage through a microscope, the first image being an image of the localarea of the smear. The image processing component is connected to theimage capturing component and configured to: process the first image, toobtain a first feature image; obtain, according to the first featureimage, a second feature image corresponding to the first feature image,the second feature image and the first feature image having the samesize; obtain a third feature image according to an image obtained byoverlaying the first feature image and the second feature image, afeature point in the third feature image indicating a possibility thatone of the multiple objects to be tested is an abnormal object; obtain asecond image according to the third feature image, the second image beindicating a location of an abnormal object in the first image. Theocular lens is connected to the image capturing component and configuredto display the first image. The augmented reality component is connectedto the image processing component and configured to project the secondimage into the ocular lens, so that the ocular lens displays the secondimage superimposed on the first image.

According to another aspect, a non-transitory storage medium isprovided, the storage medium storing at least one computer program, andthe at least one computer program, when executed by a processor, causesthe processor to perform: processing a first image to obtain a firstfeature image, the first image being an image of a local area of a smearcaptured by a microscope, and the local area including multiple objectsto be tested; obtaining a second feature image corresponding to thefirst feature image, the second feature image and the first featureimage having a same size; obtaining a third feature image according toan image obtained by overlaying the first feature image and the secondfeature image, a feature point in the third feature image indicating apossibility that one of the multiple objects is an abnormal object;obtaining a second image according to the third feature image; anddisplaying the second image superimposed on the first image.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a structural block diagram of an image display system based onartificial intelligence according to an embodiment of the application;

FIG. 2 is a schematic structural diagram of a microscope based onartificial intelligence according to an embodiment of the presentdisclosure;

FIG. 3 is a flowchart of an image display method based on artificialintelligence according to an embodiment of the present disclosure;

FIG. 4 is a flowchart of another image display method based onartificial intelligence according to an embodiment of the presentdisclosure;

FIG. 5 is a flowchart of obtaining a second image according to anembodiment of the present disclosure;

FIG. 6 is a schematic diagram of a microscope according to an embodimentof the present disclosure;

FIG. 7 is a flowchart of another image display method based onartificial intelligence according to an embodiment of the presentdisclosure;

FIG. 8 is a block diagram of an image display apparatus based onartificial intelligence according to an embodiment of the presentdisclosure;

FIG. 9 is a structural block diagram of a computer device according toan embodiment of the present disclosure; and

FIG. 10 is a schematic structural diagram of a server according to anembodiment of the present disclosure.

DESCRIPTION OF EMBODIMENTS

The following briefly describes technologies that may be used in theembodiments of the present disclosure:

Artificial intelligence (AI) is a theory, method, technology, andapplication system in which a digital computer or a machine controlledby a digital computer is used to simulate, extend, and expand humanintelligence, sense an environment, acquire knowledge, and use theknowledge to obtain an optimal result. In other words, AI is acomprehensive technology of computer science, which attempts tounderstand the essence of intelligence and produce a new type ofintelligent machine that can react in a similar way to humanintelligence. AI is to study design principles and implementationmethods of various intelligent machines, so that the machines have thefunctions of perception, reasoning, and decision-making.

Computer vision (CV) is a science that studies how to enable a machineto “see”, and to be specific, to implement machine vision such asrecognition, tracking, measurement, and the like for a target by using acamera and a computer in replacement of human eyes, and further performgraphic processing, so that the computer processes the target into animage more suitable for human eyes to observe, or more suitable to betransmitted to an instrument for detection. As a scientific discipline,the CV studies related theories and technologies and attempts toestablish an AI system that can obtain information from images ormultidimensional data. The CV technologies generally includetechnologies such as image processing, image recognition, image semanticunderstanding, image retrieval, optical character recognition (OCR),video processing, video semantic understanding, video content/behaviorrecognition, 3D object reconstruction, a 3D technology, virtual reality,augmented reality, synchronous positioning, and map construction, andfurther include biometric feature recognition technologies such ascommon face recognition and fingerprint recognition.

Machine Learning (ML) is a multi-field interdiscipline, and relates to aplurality of disciplines such as the probability theory, statistics, theapproximation theory, convex analysis, and the algorithm complexitytheory. ML specializes in studying how a computer simulates orimplements a human learning behavior to obtain new knowledge or skills,and reorganize an existing knowledge structure, so as to keep improvingperformance of the computer. ML is the core of AI, is a basic way tomake the computer intelligent, and is applied to various fields of AI.The ML and deep learning generally include technologies such as anartificial neural network, a belief network, reinforcement learning,transfer learning, inductive learning, and learning from demonstrations.

TCT is short for thinprep cytologic test. In TCT, a liquid-basedthin-layer cell test system tests cervical cells and performs cytologicclassification diagnosis. TCT is a more advanced cytologic testtechnology of cervical cancer in the world. Compared with theconventional cervical Pap smear test, TCT significantly improvessatisfaction of a specimen and the detection rate of an abnormalcervical cell.

The embodiments of the present disclosure provide an image displaymethod based on artificial intelligence. The method may be applied to ascenario in which a medical practitioner observes a smear with amicroscope based on artificial intelligence. For example, the smear maybe various cervical liquid-based smears (for example, a sedimentationsmear, a membrane-based smear, and a cell smear) produced based on thethin-layer liquid-based cytologic technique. When performingpathological test, a medical practitioner may observe a produced smearwith a microscope based on artificial intelligence, to determine whethera lesion is present.

The following describes an implementation environment of the imagedisplay method based on artificial intelligence. FIG. 1 is a structuralblock diagram of an image display system 100 based on artificialintelligence according to an embodiment of the application. The imagedisplay system based on artificial intelligence may include: amicroscope 110 based on artificial intelligence and a computer device120. In some embodiments, the system further includes a server 130 and adatabase 140.

The microscope 110 may be an electronic microscope integrated with animage capturing component. The electronic microscope is provided with animage output interface, configured to transmit a captured microscopicimage to the computer device 120 or the server 120. The image outputinterface may be a wired interface, such as a universal serial bus (USB)interface, a high definition multimedia interface (HDMI), or an Ethernetinterface. Alternatively, the image output interface may be a wirelessinterface, such as a wireless local area network (WLAN) interface or aBluetooth interface. Correspondingly, as the type of the image outputinterface varies, the microscopic image may be transmitted in multiplemanners. For example, the captured microscopic image is transmitted tothe computer device 120 in a wired manner or a wireless short distancemanner, or the computer device 120 sends the received microscopic imageto the server 130, or the captured microscopic image is transmitted tothe computer device 120 or the server 130 through a local area networkor Internet.

The computer device 120 may be a smartphone, a tablet computer, anotebook computer, a desktop computer, a smart watch, or the like, butis not limited thereto. An application program for processing theobtained microscopic image may be installed and run on the computerdevice 120. After obtaining the microscopic image, the computer device120 may process the microscopic image through the application programand send a processing result to the microscope 110, and the microscope110 displays the processing result in the ocular lens.

In the system shown in FIG. 1, the computer device 120 and themicroscope 110 are physical devices that are physically separated fromeach other. In one embodiment, the computer device 120 and themicroscope 110 may be integrated into a single physically device. Forexample, the microscope 110 may be an intelligent microscope with acomputing function of the computer device 120.

For example, FIG. 2 is a schematic structural diagram of a microscopebased on artificial intelligence according to an embodiment of thepresent disclosure. As shown in FIG. 2, the microscope 110 is applied tothe image display system based on artificial intelligence shown inFIG. 1. The microscope 110 may include an image capturing component 111,an objective lens 112, an ocular lens 113, an augmented realitycomponent 114, and an image processing component 115. In someembodiments, the microscope 110 may further include a microscope body116 or the like.

The objective lens 112 is configured to magnify a local area of a smearincluding multiple to-be-tested objects. The image capturing component111 is connected to the objective lens 112 and is configured to obtain afirst image, the first image being an image obtained by capturing animage of the local area of the smear including multiple to-be-testedobjects. The image processing component 115 is connected to the imagecapturing component 111 and is configured to process the first image, toobtain a first feature image. The image processing component 115 isfurther configured to obtain, according to the first feature image, asecond feature image corresponding to the first feature image, thesecond feature image and the first feature image having the same size.The image processing component 115 is further configured to obtain athird feature image according to an image obtained by overlaying thefirst feature image and the second feature image, a feature point in thethird feature image indicating a possibility that one of the multipleobjects to be tested is an abnormal object. The image processingcomponent 115 is further configured to obtain a second image accordingto the third feature image, the second image indicating a location of anabnormal object in the first image. The ocular lens 113 is connected tothe image capturing component 111 and is configured to display the firstimage captured by the image capturing component 111. The augmentedreality component 114 is connected to the image processing component 115and is configured to project the second image into the ocular lens 113based on the augmented reality (AR) technology, so that the ocular lens113 displays the second image superimposed on the first image. Themicroscope body 116 is configured to provide physical support for theforegoing components, including structure support, energy support, andthe like. The microscope 110 may further include another functionalcomponent. This is not limited in the embodiments of the presentdisclosure.

The server 130 may be an independent physical server, or may be a servercluster including a plurality of physical servers or a distributedsystem, or may be a cloud server providing basic cloud computingservices, such as a cloud service, a cloud database, cloud computing, acloud function, cloud storage, a network service, cloud communication, amiddleware service, a domain name service, a security service, a contentdelivery network (CDN), big data, and an artificial intelligenceplatform.

The computer device 120 and the server 130 may be directly or indirectlyconnected in a wired or wireless communication manner. This is notlimited in the embodiments of the present disclosure.

The server 130 is configured to provide the computer device 120 with aservice of processing the microscopic image. In some embodiments, theserver 130 is responsible for performing primary processing on amicroscopic image, and the computer device 120 is responsible forperforming secondary processing on a microscopic image. Alternatively,the server 130 is responsible for performing secondary processing on amicroscopic image, and the computer device 120 is responsible forperforming primary processing on a microscopic image. Alternatively, theserver 130 or the computer device 120 may separately process amicroscopic image.

The database 140 may be a Redis database or another type of database.The database 140 is configured to store various types of data.

In some embodiments, a standard communication technology and/or protocolis used for the wireless network or the wired network described above.The network is generally the Internet, but may be any network,including, but not limited to, any combination of a local area network(LAN), a metropolitan area network (MAN), a wide area network (WAN), amobile, wired, or wireless network, and a dedicated network or a virtualprivate network. In some embodiments, technologies and/or formats suchas the Hyper Text Markup Language (HTML) and the extensible markuplanguage (XML) are used for representing data exchanged through thenetwork. In addition, all or some links may be encrypted by usingconventional encryption technologies such as a secure socket layer(SSL), transport layer security (TLS), a virtual private network (VPN),and internet protocol security (IPsec). In some other embodiments,custom and/or dedicated data communication technologies may also be usedin place of or in addition to the foregoing data communicationtechnologies.

FIG. 3 is a flowchart of an image display method based on artificialintelligence according to an embodiment of the present disclosure. Theimage display method may be performed by a computer device. The computerdevice may be a single device and is integrated with a microscopicfunction. Alternatively, the computer device may be a set of multipledevices. For example, the computer device may include the microscope 110in the system shown in FIG. 1, that is, the method may be performedthrough interaction between the microscope 110 and the computer device120 in the system shown in FIG. 1. As shown in FIG. 3, the image displaymethod based on artificial intelligence includes the following steps:

301. Process a first image to obtain a first feature image based on animage classification model, the first image being an image obtained bycapturing an image of a local area of a smear including multipleto-be-tested objects through a microscope.

Step 301 is one embodiment of processing the first image to obtain thefirst feature image. In this implementation, the process of processingthe first image to obtain the first feature image is implemented basedon the image classification model. In another embodiment, the firstimage may be alternatively processed in another manner rather than basedon the image classification model. For example, the first image isprocessed to obtain the first feature image directly through an imageprocessing script. This is not limited in this embodiment of the presentdisclosure.

In this embodiment of the present disclosure, the smear is a smearplaced on an objective lens stage of the microscope. The microscope maymagnify the local area of the smear through the objective lens, and thencapture a magnified microscopic image through an image capturingcomponent and use the captured image as the first image. For example,the smear may be a cervical liquid-based smear or another smear producedbased on the thin-layer liquid-based cytologic technique. The imageclassification model may be a model obtained based on machine learningand include an already trained neural network, and convolutionprocessing may be performed on the first image to obtain the firstfeature image based on the already trained neural network. That is,multiple layers of neural network convolution structures may be used asthe image classification model, and the image classification modelincludes multiple convolution layers. The first image may be inputtedinto the image classification model, and a convolution layer in theimage classification model performs convolution processing on the firstimage, to obtain the first feature image.

302. Obtain, according to the first feature image based on the imageclassification model, a second feature image corresponding to the firstfeature image, the second feature image and the first feature imagehaving the same size.

Step 302 is one embodiment of obtaining the second feature imagecorresponding to the first feature image. In this implementation, theprocess of obtaining the second feature image corresponding to the firstfeature image is implemented based on the image classification model. Inanother embodiment, the first feature image may be processed to obtainthe second feature image in another manner rather than based on theimage classification model. For example, the first feature image isprocessed to obtain the second feature image directly through an imageprocessing script. This is not limited in this embodiment of the presentdisclosure.

In this embodiment of the present disclosure, convolution anddeconvolution processing may be performed on the first feature imagebased on the image classification model, to obtain the second featureimage having the same size as the first feature image. The imageclassification model may include a trained neural network, that is,multiple layers of neural network convolution structures may be used asthe image classification model, and the image classification modelincludes multiple convolution layers. In step 302, after the first imageis processed to obtain the first feature image based on the imageclassification model, the convolution layer in the image classificationmodel may further perform convolution and deconvolution processing onthe first feature image to obtain the second feature image.

303. Overlay the first feature image and the second feature image basedon the image classification model, to obtain a third feature image basedon an image obtained after overlay, a feature point in the third featureimage indicating a possibility that one of the multiple objects to betested is an abnormal object.

Step 303 is one embodiment of overlaying the first feature image and thesecond feature image, to obtain the third feature image based on theimage obtained after overlay. In this implementation, the process ofoverlaying the two feature images to obtain the third feature imagebased on the image obtained after overlay is implemented based on theimage classification model. In another embodiment, the first featureimage, the second feature image, and the image obtained after overlaymay be processed to obtain the third feature image in another mannerrather than based on the image classification model. For example, thefirst feature image and the second feature image are overlaid directlythrough an image processing script, to obtain the third feature imagebased on the image obtained after overlay. This is not limited in thisembodiment of the present disclosure.

In this embodiment of the present disclosure, the first feature imageand the second feature image that have the same size may be overlaid toobtain a new image based on the image classification model, and the newimage obtained after overlay and the two feature images that are notoverlaid have the same size. Convolution processing may be performed onthe new image obtained after overlay, to obtain the third feature image.A feature value of each feature point in the third feature imageindicates possibility that a to-be-tested object is an abnormal object.The higher possibility indicates a larger feature value.

In some embodiments, the image classification model may include atrained neural network, that is, multiple layers of neural networkconvolution structures may be used as the image classification model,and the image classification model includes multiple convolution layers.In step 303, after the first feature image and the second feature imageare obtained, the two feature images may be overlaid based on the imageclassification model, and then the convolution layer in the imageclassification model performs convolution processing on the new imageobtained after overlay, to obtain the third feature image.

304. Obtain a second image according to the third feature image, thesecond image indicating a location of an abnormal object in the firstimage.

In this embodiment of the present disclosure, a location of an abnormalobject may be obtained based on the obtained third feature image, toobtain the second image indicating the location of the abnormal object.

305. Display the second image superimposed on the first image.

In this embodiment of the present disclosure, an augmented realitycomponent of the microscope may output the first image and the secondimage to the ocular lens of the microscope for display. In this way, themedical practitioner may observe, while observing the first imagethrough the ocular lens, the second image displayed through overlay.Therefore, the location of the abnormal object is identified for themedical practitioner.

In this embodiment of the present disclosure, the microscope captures animage of the local area of the smear, then the captured first image isprocessed based on the image classification model, the first featureimage obtained through processing and the corresponding second featureimage are overlaid, the third feature image in which a feature pointindicates the possibility that a to-be-tested object is an abnormalobject is outputted based on the image obtained after overlay, then thesecond image that indicates the location of the abnormal object in thefirst image is obtained based on the third feature image, and finallythe second image is displayed by overlaying the second image on thefirst image. In this way, when interpreting the smear, the medicalpractitioner may make a judgment in real time when the local area of thesmear includes the abnormal object. The medical practitioner does notneed to confirm again. This shortens the working procedure of themedical practitioner and improves screening efficiency.

FIG. 4 is a flowchart of another image display method based onartificial intelligence according to an embodiment of the presentdisclosure. As shown in FIG. 4, in this embodiment of the presentdisclosure, a computer device is used as an example, and the computerdevice may be a terminal or a server. As can be seen from the embodimentshown in FIG. 3, some steps in the image display method based onartificial intelligence may be performed based on the imageclassification model. In the embodiment shown in FIG. 4, only theexample of performing these steps based on the image classificationmodel is used, but these steps are not limited to this implementation.The image display method based on artificial intelligence includes thefollowing steps:

401. A computer device obtains a first image, the first image being animage obtained by capturing an image of a local area of a smearincluding multiple to-be-tested objects through a microscope.

In this embodiment of the present disclosure, the microscope has aparticular field of view range, and may cover the local area of thesmear. The location of the smear may be moved, to change an area of thesmear in the current field of view of the microscope. The microscope maymagnify the area of the smear in the current field of view through theobjective lens. The computer device may capture an image of themagnified area of the smear through the image capturing component, anduse the captured microscopic image as the first image. The first imageis consistent with the local area of the smear observed by a medicalpractitioner through the ocular lens of the microscope. As the power ofthe objective lens used by the microscope to capture an image varies,the magnification power of the captured first image is different.

When the computer device detects that the location of the smear moves,or when the microscope is switched from the current objective lens to anobjective lens with another power, the computer device may capture animage again through the image capturing component, to obtain a newmicroscopic image. The new microscopic image is inconsistent with anarea of the smear corresponding to the first image captured before thelocation of the smear moves, or a magnification power of the newmicroscopic image is different from that of a first microscopic imagecaptured before the objective lens is switched.

The computer device may process the obtained first image by invoking theimage classification model.

In one embodiment, the computer device may obtain an image attribute ofthe first image before invoking the image classification model, andobtain a corresponding image classification model based on the imageattribute. The image attribute may be used to indicate a pathologicalanalysis type of the first image. The computer device may invoke,according to the pathological analysis type, an image classificationmodel corresponding to the pathological analysis type. That is, afterobtaining the pathological analysis type of the first image, thecomputer device may invoke the image classification model correspondingto the pathological analysis type. In some embodiments, one pathologicalanalysis type may correspond to at least one image classification model,and image classification models are different from each other in thatmagnification powers of sample images used for training are different.The computer device may select an image classification model whosecorresponding magnification power is the same as the targetmagnification power from the at least one image classification modelaccording to the target magnification power used when the first image iscaptured. The image classification model is invoked based on the imageattribute of the first image, and various image classification modelscorresponding to different objective lens powers are set. Therefore,application in various pathological analysis scenarios may beimplemented and the application scope is increased.

For example, the first image is an image obtained by capturing an imageof a cervical liquid-based smear. The computer device obtains thepathological analysis type of the first image as a cervical canceranalysis type, and then invokes an image classification model foranalyzing positive cells of cervical cancer.

402. The computer device processes the first image to obtain a firstfeature image based on the image classification model.

In this embodiment of the present disclosure, the computer device mayperform convolution processing on the first image through an alreadytrained neural network in the image classification model, to obtain thefirst feature image.

In one embodiment, training steps of the image classification model maybe as follows: The computer device obtains a training sample image, andobtains a training target image according to the training sample image.The training sample image is an image in which a location of an abnormalobject is marked, and the training target image is a binary image and isused to indicate the abnormal object. The computer device may input thetraining sample image to an image classification model to be trained, toobtain a first sample feature image outputted by the to-be-trained imageclassification model. The computer device then performs parameteradjustment according to the first sample feature image and the trainingtarget image. In response to that a training end condition is satisfied,the computer device may use a model obtained through training as theimage classification model. When the image classification model is amodel for classifying positive cells, the training sample image may bean image including positive cells and negative cells at differentratios. For example, ratios of positive cells to negative cells are 5:1,3:1, and 2:1. The training end condition may be: reaching a targetquantity of training times, or the model converges, or the like. This isnot limited in this embodiment of the present disclosure. The targetquantity of training times may be set by a relevant technical personaccording to the requirement. This is not limited in this embodiment ofthe present disclosure. In some embodiments, to increase the recallratio of the training sample image including positive cells, thecomputer device may increase the loss weight of the training sampleimage including positive cells when training the image classificationmodel. Because the image classification model is trained according tothe characteristic of cancer lesion cell screening with reference to thecaptured training sample image, the image classification model may beeffectively trained to identify positive cells.

In one embodiment, the location of an abnormal object in the trainingsample image may be marked by a professional, for example, a medicalexpert marks the location of a positive cell. Correspondingly, thecomputer device may obtain the training target image according to thetraining sample image in the following step: the computer deviceperforms value conversion on the training sample image, to obtain atwo-channel training target image. The training target image is a binaryimage. Value conversion steps may be: the computer device uses an areamarked in the training sample image as a target area, sets a pixel valueof a target area to 1 and sets a pixel value of a non-target area to 0as a channel of the training target image, and sets the pixel value ofthe target area to 0 and sets the pixel value of the non-target area to1 as another channel of the training target image. The binary trainingtarget image is obtained, so that the training target image may be usedas a label to train the image classification model under supervision. Inthis way, the model obtained through training may output an image thatis similar to the training target image.

In one embodiment, when convolution processing is performed on the firstimage based on the image classification model, the size of an imageoutputted by each convolution layer may be half the size of an imageinputted to the convolution layer. The computer device may obtain thequantity of convolution layers of the image classification modelaccording to the target scaling ratio. The target scaling ratio is usedto indicate a ratio between the first sample feature image and thetraining sample image. Correspondingly, the computer device may obtainthe quantity of convolution layers of the image classification model inthe following steps: The computer device may obtain a target sizeaccording to an image attribute of the training sample image, where thetarget size is used to indicate an actual size represented by a pixel inthe training sample image. Then, the computer device may obtain thetarget scaling ratio according to the target size and a reference cellsize. The computer device obtains the quantity of convolution layers ofthe image classification model according to the target scaling ratio.The reference cell size may be the size of a small squamous cell, or thesize of a cell in other base liquid. This is not limited in theembodiments of the present disclosure. The actual size is an actual sizerepresented by a pixel, that is, the size that is not magnified. Thecomputer device may obtain the target size according to the imageattribute of the training sample image in the following steps: thecomputer device may obtain the image attribute of the training sampleimage, obtain a sample magnification power used when the training sampleimage is captured and a pixel size that exists when the training sampleimage is captured, and then obtain the target size according to thesample magnification power and the pixel size. For example, themagnification power is 10 and the diameter of a squamous cell is 10microns. In this case, the diameter of a magnified squamous cell in thetraining sample image is 100 microns. If the pixel size that exists whenthe training sample image is captured by the image capturing componentis 50 microns, about 4 pixels in the training sample image cover asquamous cell. Because the actual diameter of a squamous cell is 10microns, the actual size of a pixel in the training sample image is 5microns, that is, the target size is 5 microns. Because the targetscaling ratio is obtained based on the reference cell size and the pixelsize of each pixel in the training sample image, each cell in the firstsample feature image may be covered by at least one pixel. More pixelseach of which covers one cell indicate the higher resolution of thesecond image and the clearer cell image.

For example, the pixel size of each pixel in the training sample imageis 824 microns, and the reference cell size is the size of a squamouscell. The size of a small squamous cell, that is, the diameter, isbetween 10 microns and 20 microns. For example, the reference cell sizeis set to 10 microns. To reach the objective that one pixel covers onesquamous cell, when the magnification power of the microscope is 10, thetarget size is 82.4 microns, and the target scaling ratio is 82.4/10 andis about 8. To reach the objective that four pixels cover one squamouscell, the target scaling ratio may be (82.4×2)/10 and is about 16.

403. The computer device obtains, according to the first feature imagebased on the image classification model, a second feature imagecorresponding to the first feature image, the second feature image andthe first feature image having the same size.

In this embodiment of the present disclosure, after performingconvolution processing on the first feature image, the computer devicemay perform deconvolution processing on the first feature image, toobtain the second feature image. Correspondingly, the computer devicemay obtain the second feature image in the following steps: The computerdevice may perform convolution processing on the first feature imagebased on the image classification model, to obtain a fourth featureimage; and then perform deconvolution processing on the fourth featureimage based on the image classification model, to obtain the secondfeature image corresponding to the first feature image. The firstfeature image and the second feature image have the same size anddifferent quantities of channels.

Alternatively, after performing convolution processing on the firstfeature image, the computer device may perform up-sampling processing toobtain the second feature image. This is not limited in the embodimentsof the present disclosure.

404. The computer device overlays the first feature image and the secondfeature image based on the image classification model, to obtain a thirdfeature image based on an image obtained after overlay, a feature pointin the third feature image indicating a possibility that one of themultiple objects to be tested is an abnormal object.

In this embodiment of the present disclosure, the computer device mayoverlay the first feature image and the second feature image based onthe image classification model, to obtain a fifth feature image, wherethe fifth feature image and the first feature image have the same sizeand the fifth feature image and the second feature image also have thesame size, and channels of the fifth feature image include channels ofthe first feature image and channels of the second feature image. Thecomputer device may perform convolution processing on the fifth featureimage based on the image classification model, to obtain the thirdfeature image. The third feature image is a binary image, a pixel pointin the image is a feature point, and a feature value of each featurepoint is between 0 and 1 and is used to indicate possibility that ato-be-tested object is an abnormal object. The third feature imageincludes two channels, and one channel is a negative value channel andthe other channel is a positive value channel. In the positive valuechannel, if possibility that a to-be-tested object is an abnormal objectis higher, a value of a feature point corresponding to the to-be-testedobject is closer 1, and otherwise, is closer to 0. In the negative valuechannel, if possibility that a to-be-tested object is an abnormal objectis higher, a value of a feature point corresponding to the to-be-testedobject is closer 0, and otherwise, is closer to 1. The pixel value ofeach pixel in the third feature image is between 0 and 1, which issimilar to a thermodynamic diagram. Therefore, the third feature imagemay also be referred to as a two-channel thermodynamic diagram.

405. The computer device obtains a second image according to the thirdfeature image, the second image indicating a location of an abnormalobject in the first image.

In this embodiment of the present disclosure, after obtaining the thirdfeature image, the computer device may obtain a target channel image inthe third feature image. A feature value of each feature point in thetarget channel image is between 0 and 1. A feature point whose featurevalue is not zero indicates that a corresponding object is abnormal. Forany feature point whose feature value is not zero, in response to thatthe feature value of the feature point is not less than a targetthreshold, the computer device adjusts the feature value of the featurepoint to 1. In response to that the feature value of the feature pointis less than the target threshold, the computer device adjusts thefeature value of the feature point to 0. Then, the computer device mayadjust a size of the target channel image in which the feature value isadjusted, so that the size is the same as that of the first image, andobtain edge feature points of an image area whose feature values are 1in the target channel image whose size is adjusted, to obtain the secondimage. That is, for the image area whose feature values are 1, thecomputer device may reserve only the edge area of the image area and setall other areas to be transparent, to obtain the second image. Thetarget threshold may be 0.5, 0.65, 0.7, or the like. This is not limitedin the embodiments of the present disclosure.

For example, the scaling ratio between the third feature image and thefirst image is 8, and the length and the width of the third featureimage are both ⅛ of those of the first image. The computer deviceperforms value adjustment on a positive value channel image in the thirdfeature image to obtain an image with a pixel value of 0 or 1, and thenmagnifies the obtained image by 8 times according to the scaling ratioto obtain the second image. In the second image, an area with a pixelvalue of 1 indicates a possible location of a positive cell in the firstimage.

For the target channel image, the computer device may further remove animpurity area and an area that is less than a target size from thetarget channel image. The computer device may use an image area whoseshape differs greatly from that of the nucleus as the impurity area. Thecomputer may obtain the size of each continuous pixel value areaaccording to continuity between pixels, and remove an image area whosesize is less than the target size. Because the impurity area and thesmall image area are removed, the remaining image area includes largecells, such as positive cells or negative cells.

To make the step of obtaining the second image by the computer deviceaccording to step 401 and step 405 clearer, refer to FIG. 5. FIG. 5 is aflowchart of obtaining a second image according to an embodiment of thepresent disclosure. As shown in FIG. 5, the first image includes threecells, that is, three to-be-tested objects. The height of the firstimage is H, the width of the first image is W, and the quantity ofchannels of the first image is 3. H and W are positive numbers greaterthan 0. The computer device performs convolution processing on the firstimage to obtain a feature image with a height H/2, a width W/2, and aquantity of channels 96. The computer device performs convolutionprocessing on the obtained feature image again to obtain a feature imagewith a height H/4, a width W/4, and a quantity of channels 144. Thecomputer device performs convolution processing on the obtained featureimage to obtain a feature image with a height H/8, a width W/8, and aquantity of channels 288, where the feature image is the first featureimage. The computer device performs convolution processing on the firstfeature image to obtain a fourth feature image with a height H/16, awidth W/16, and a quantity of channels 528. The computer device performsdeconvolution processing on the obtained fourth feature image to obtaina feature image with a height H/8, a width W/8, and a quantity ofchannels 528, where the feature image is the second feature image. Thecomputer device overlays the first feature image and the second featureimage to obtain a fifth feature image with a height H/8, a width W/8,and a quantity of channels 816, and then performs convolution processingon the fifth feature image to obtain a feature image with a height H/8,a width W/8, and a quantity of channels 2, where the feature image isthe third feature image. The computer device obtains a positive valuechannel image in the third feature image, then performs value processingon the positive value channel image based on the target threshold, andfinally magnifies the positive value channel image in which the featurevalue is adjusted to a size that is the same as that of the first image.

406. The computer device displays the second image superimposed on thefirst image.

In this embodiment of the present disclosure, after obtaining the secondimage, the computer device may project the second image to an opticalpath of the ocular lens of the microscope, that is, display both thefirst image and the second image in the ocular lens of the microscope.The second image is displayed by overlaying the second image on thefirst image, so that the location of an abnormal object such as thelocation of a positive cell in the first image can be indicated. Thishelps a medical practitioner to view.

To make steps described in step 401 to step 406 clearer, refer to FIG.6. FIG. 6 is a schematic diagram of a microscope according to anembodiment of the present disclosure. As shown in FIG. 6, the microscopeis a computer device 600 integrated with multiple components, includingan image capturing component 111, an image processing component 115, anaugmented reality component 114, and the like. The image capturingcomponent 111 is configured to capture a first image 601, the imageprocessing component 115 is configured to process the first image toobtain a second image 602, and the augmented reality component 114 isconfigured to project the second image into an ocular lens 113, so thatthe ocular lens 113 displays the second image 602 by overlaying thesecond image 602 on the first image 601.

Step 401 to step 406 are example embodiments of the image display methodbased on artificial intelligence provided in the embodiments of thepresent disclosure. In another embodiment, the method may be performedthrough interaction between multiple devices such as a microscope and aserver. FIG. 7 is a flowchart of another image display method based onartificial intelligence according to an embodiment of the presentdisclosure. As shown in FIG. 7, the method includes the following steps:Step 701. The microscope captures a first image and sends the firstimage to the server. Step 702. The server queries a corresponding imageclassification model from a database according to an image attribute ofthe first image. Step 703. The server invokes the image processing modelto perform convolution processing on the first image to obtain a thirdfeature image. Step 704. The server performs value processing and sizeadjustment on a target channel image of the third feature image toobtain a second image. Step 705. The server returns the second image tothe microscope. Step 706. The microscope displays the second imagesuperimposed on the first image displayed in an ocular lens.

The computer device may further output the first image and the secondimage displayed through overlay to a display device for display throughan image output interface. The display device may be a local displaydevice or a remote display device. For example, the microscope may beprovided with an electric stage. The electric stage is configured toadjust the location of the smear, to change the first image capturedunder current field of view of the microscope. The computer device maysynchronously display and output the captured first image and secondimage. Alternatively, the computer device may send the first image andthe second image to the remote end in the form of video streams. Amedical practitioner at the remote end views the first image and thesecond image and makes a judgment. This may be applied to a remoteconsultation scenario.

In this embodiment of the present disclosure, the microscope captures animage of the local area of the smear, then the captured first image isprocessed based on the image classification model, the first featureimage obtained through processing and the corresponding second featureimage are overlaid, the third feature image in which a feature pointindicates the possibility that a to-be-tested object is an abnormalobject is outputted based on the image obtained after overlay, then thesecond image that indicates the location of the abnormal object in thefirst image is obtained based on the third feature image, and finallythe second image is displayed by overlaying the second image on thefirst image. In this way, when interpreting the smear, the medicalpractitioner may make a judgment in real time when the local area of thesmear includes the abnormal object. The medical practitioner does notneed to confirm again. This shortens the working procedure of themedical practitioner and improves screening efficiency.

FIG. 8 is a block diagram of an image display apparatus based onartificial intelligence according to an embodiment of the presentdisclosure. The apparatus is configured to perform the steps of theimage display method based on artificial intelligence. Referring to FIG.8, the apparatus includes: an image processing module 801, an imageobtaining module 802, and an image display module 803.

The image processing module 801 is configured to process a first imageto obtain a first feature image, the first image being an image obtainedby capturing an image of a local area of a smear including multipleto-be-tested objects through a microscope.

The image processing module 801 is further configured to obtain a secondfeature image corresponding to the first feature image, the secondfeature image and the first feature image having the same size.

The image processing module 801 is further configured to obtain a thirdfeature image according to an image obtained by overlaying the firstfeature image and the second feature image, a feature point in the thirdfeature image indicating a possibility that one of the multiple objectsto be tested is an abnormal object.

The image obtaining module 802 is configured to obtain a second imageaccording to the third feature image, the second image indicating alocation of an abnormal object in the first image.

The image display module 803 is configured to display the second imagesuperimposed on the first image.

In one embodiment, the image processing module 801 is further configuredto: perform convolution processing on the first feature image, to obtaina fourth feature image; and perform deconvolution processing on thefourth feature image, to obtain the second feature image correspondingto the first feature image.

In one embodiment, the image processing module 801 is further configuredto: overlay the first feature image and the second feature image, toobtain a fifth feature image, where the fifth feature image and thefirst feature image have the same size, and channels of the fifthfeature image include channels of the first feature image and channelsof the second feature image; and perform convolution processing on thefifth feature image, to obtain the third feature image.

In one embodiment, the third feature image is a two-channel image; andthe image processing module 801 is further configured to: obtain atarget channel image in the third feature image, where in the targetchannel image, a feature point whose feature value is not zero indicatesthat a corresponding object is abnormal; for any feature point in thetarget channel image whose feature value is not zero, in response tothat the feature value of the feature point is not less than a targetthreshold, adjust the feature value of the feature point to 1; inresponse to that the feature value of the feature point is less than thetarget threshold, adjust the feature value of the feature point to 0;adjust a size of the target channel image in which the feature value isadjusted, so that the size is the same as that of the first image; andobtain edge feature points of an image area whose feature values are 1in the target channel image whose size is adjusted, to obtain the secondimage.

In one embodiment, the image processing module 801 is further configuredto remove an impurity area and an area that is less than a target sizefrom the target channel image.

In one embodiment, the processes of processing a first image, obtaininga second feature image, and obtaining a third feature image according toan image obtained by overlaying the first feature image and the secondfeature image are implemented based on an image classification model.

In one embodiment, the apparatus further includes:

an image obtaining module 802, configured to obtain the first image;

a first determining module, configured to obtain a pathological analysistype of the first image according to an image attribute of the firstimage; and

a model invoking module, configured to invoke the image classificationmodel corresponding to the pathological analysis type.

In one embodiment, the image attribute includes a target magnificationpower used for capturing the first image; and the model invoking moduleis further configured to: obtain, according to the pathological analysistype, at least one candidate image classification model corresponding tothe pathological analysis type; and in response to that a sample imagemagnification power used to train a first candidate image classificationmodel is the same as the target magnification power, use the firstcandidate image classification model as the image classification model.

In one embodiment, the apparatus further includes a model trainingmodule, configured to: obtain a training sample image, and obtain atraining target image according to the training sample image, where thetraining sample image is an image in which a location of an abnormalobject is marked, and the training target image is a binary image and isused to indicate the abnormal object; input the training sample image toan image classification model to be trained, to obtain a first samplefeature image outputted by the to-be-trained image classification model;perform parameter adjustment according to the first sample feature imageand the training target image; and in response to that a training endcondition is satisfied, use a model obtained through training as theimage classification model.

In one embodiment, the apparatus further includes:

a second determining module, configured to obtain a target sizeaccording to an image attribute of the training sample image, where thetarget size is used to indicate an actual size represented by a pixel inthe training sample image;

the second determining module is further configured to obtain a targetscaling ratio according to the target size and a reference cell size,where the target scaling ratio is used to indicate a ratio between thefirst sample feature image and the training sample image; and

the second determining module is further configured to obtain a quantityof convolution layers of the image classification model according to thetarget scaling ratio.

In one embodiment, the second determining module is further configuredto: according to the image attribute of the training sample image,obtain a sample magnification power used when the training sample imageis captured and a pixel size that exists when the training sample imageis captured; and obtain the target size according to the samplemagnification power and the pixel size.

The term unit (and other similar terms such as subunit, module,submodule, etc.) in this disclosure may refer to a software unit, ahardware unit, or a combination thereof. A software unit (e.g., computerprogram) may be developed using a computer programming language. Ahardware unit may be implemented using processing circuitry and/ormemory. Each unit can be implemented using one or more processors (orprocessors and memory). Likewise, a processor (or processors and memory)can be used to implement one or more units. Moreover, each unit can bepart of an overall unit that includes the functionalities of the unit.

In this embodiment of the present disclosure, the image capturing moduleof the microscope captures an image of the local area of the smear, thenthe image processing module processes the captured first image, overlaysthe first feature image obtained through processing and thecorresponding second feature image, outputs the third feature image inwhich a feature point indicates the possibility that a to-be-testedobject is an abnormal object based on the image obtained after overlay,and then obtains the second image that indicates the location of theabnormal object in the first image based on the third feature image, andfinally the image display module displays the second image superimposedon the first image. In this way, when interpreting the smear, themedical practitioner may make a judgment in real time when the localarea of the smear includes the abnormal object. The medical practitionerdoes not need to confirm again. This shortens the working procedure ofthe medical practitioner and improves screening efficiency.

When the image display apparatus based on artificial intelligenceprovided in the above embodiments runs an application program, onlydivision of the above functional modules is used as an example. Inpractical applications, the above functions can be allocated todifferent functional modules for implementation according to therequirement, that is, the internal structure of the apparatus is dividedinto different functional modules, to perform all or some of thefunctions described above. In addition, the embodiments of the imagedisplay apparatus based on artificial intelligence and the image displaymethod based on artificial intelligence provided in the aboveembodiments belong to the same concept. For the specific implementationprocess, refer to the method embodiments. Details are not describedherein again.

An embodiment of the present application further provides a computerdevice. The computer device may vary greatly due to differentconfigurations or performance, and may include one or more centralprocessing units (CPU) and one or more memories. At least one computerprogram is stored in the memory. The at least one computer program isloaded and executed by the processor to perform the following steps:processing a first image to obtain a first feature image, the firstimage being an image obtained by capturing an image of a local area of asmear including multiple to-be-tested objects through a microscope;obtaining a second feature image corresponding to the first featureimage, the second feature image and the first feature image having thesame size; obtaining a third feature image according to an imageobtained by overlaying the first feature image and the second featureimage, a feature point in the third feature image indicating apossibility that one of the multiple objects to be tested is an abnormalobject; obtaining a second image according to the third feature image,the second image indicating a location of an abnormal object in thefirst image; and displaying the second image superimposed on the firstimage.

In some embodiments, the at least one computer program is loaded by theprocessor to specifically perform the following steps: performingconvolution processing on the first feature image, to obtain a fourthfeature image; and performing deconvolution processing on the fourthfeature image, to obtain the second feature image corresponding to thefirst feature image.

In some embodiments, the at least one computer program is loaded by theprocessor to specifically perform the following steps: overlaying thefirst feature image and the second feature image, to obtain a fifthfeature image, where the fifth feature image and the first feature imagehave the same size, and channels of the fifth feature image includechannels of the first feature image and channels of the second featureimage; and performing convolution processing on the fifth feature image,to obtain the third feature image.

In some embodiments, the third feature image is a two-channel image; andthe at least one computer program is loaded by the processor tospecifically perform the following steps: obtaining a target channelimage in the third feature image, where in the target channel image, afeature point whose feature value is not zero indicates that acorresponding object is abnormal; for any feature point in the targetchannel image whose feature value is not zero, in response to that thefeature value of the feature point is not less than a target threshold,adjusting the feature value of the feature point to 1; in response tothat the feature value of the feature point is less than the targetthreshold, adjusting the feature value of the feature point to 0;adjusting a size of the target channel image in which the feature valueis adjusted, so that the size is the same as that of the first image;and obtaining edge feature points of an image area whose feature valuesare 1 in the target channel image whose size is adjusted, to obtain thesecond image.

In some embodiments, the at least one computer program is further loadedby the processor to perform the following step: removing an impurityarea and an area that is less than a target size from the target channelimage.

In some embodiments, the at least one computer program is further loadedby the processor to perform the following steps: obtaining the firstimage; and obtaining a pathological analysis type of the first imageaccording to an image attribute of the first image; and invoking theimage classification model corresponding to the pathological analysistype.

In some embodiments, the image attribute includes a target magnificationpower used for capturing the first image; and the at least one computerprogram is loaded by the processor to specifically perform the followingsteps: obtaining, according to the pathological analysis type, at leastone candidate image classification model corresponding to thepathological analysis type; and in response to that a sample imagemagnification power used to train a first candidate image classificationmodel is the same as the target magnification power, using the firstcandidate image classification model as the image classification model.

In some embodiments, the image classification model is obtained throughtraining in the following steps: obtaining a training sample image, andobtaining a training target image according to the training sampleimage, where the training sample image is an image in which a locationof an abnormal object is marked, and the training target image is abinary image and is used to indicate the abnormal object; inputting thetraining sample image to an image classification model to be trained, toobtain a first sample feature image outputted by the to-be-trained imageclassification model; performing parameter adjustment according to thefirst sample feature image and the training target image; and inresponse to that a training end condition is satisfied, using a modelobtained through training as the image classification model.

In some embodiments, the at least one computer program is further loadedby the processor to perform the following steps: obtaining a target sizeaccording to an image attribute of the training sample image, where thetarget size is used to indicate an actual size represented by a pixel inthe training sample image; obtaining a target scaling ratio according tothe target size and a reference cell size, where the target scalingratio is used to indicate a ratio between the first sample feature imageand the training sample image; and obtaining a quantity of convolutionlayers of the image classification model according to the target scalingratio.

In some embodiments, the at least one computer program is loaded by theprocessor to specifically perform the following steps: according to theimage attribute of the training sample image, obtaining a samplemagnification power used when the training sample image is captured anda pixel size that exists when the training sample image is captured; andobtaining the target size according to the sample magnification powerand the pixel size.

Certainly, the server may also include other components for implementingfunctions of the device. Details are not described herein.

The computer device may be a terminal, and the terminal is describedbelow. FIG. 9 is a structural block diagram of a computer device 900according to an embodiment of the present disclosure. FIG. 9 is astructural block diagram of the computer device 900 according to anexemplary embodiment of the present disclosure. The computer device 900may be: a smartphone, a tablet computer, a notebook computer, or adesktop computer. The computer device 900 may also be referred to asuser equipment (UE), a portable computer device, a laptop computerdevice, a desktop computer device, or another name.

Generally, the computer device 900 includes a processor 901 and a memory902.

The processor 901 may include one or more processing cores such as a4-core processor or an 8-core processor. In some embodiments, theprocessor 901 may be integrated with a graphics processing unit (GPU).The GPU is configured to render and draw content that needs to bedisplayed on a display. In some embodiments, the processor 901 mayfurther include an AI processor. The AI processor is configured toprocess a computing operation related to machine learning.

The memory 902 may include one or more computer-readable storage mediathat may be non-transitory. In some embodiments, a non-transitorycomputer-readable storage medium in the memory 902 is configured tostore at least one computer program. The at least one computer programis executed by the processor 901 to implement the image display methodbased on artificial intelligence provided in the method embodiments ofthe present disclosure.

In some embodiments, the computer device 900 may further include aperipheral interface 903 and at least one peripheral. The processor 901,the memory 902, and the peripheral device interface 903 may be connectedby using a bus or a signal cable. Each peripheral may be connected tothe peripheral interface 903 by using a bus, a signal cable, or acircuit board. In some embodiments, the peripheral device includes: atleast one of a display screen 904 and a camera component 905. Theperipheral interface 903 may be configured to connect the at least oneperipheral related to Input/Output (I/O) to the processor 901 and thememory 902. The display screen 904 is configured to display a capturedimage or video. The camera component 905 is configured to acquire imagesor videos.

A person skilled in the art may understand that the structure shown inFIG. 9 does not constitute any limitation on the computer device 900,and the computer device may include more components or fewer componentsthan those shown in the figure, or some components may be combined, or adifferent component deployment may be used.

The computer device may be a server, and the server is described below.FIG. 10 is a schematic structural diagram of a server according to anembodiment of the present disclosure. The server 1000 may vary greatlydue to different configurations or performance, and may include one ormore processors (for example, central processing units, CPU) 1001 andone or more memories 1002. The memory 1002 stores at least one computerprogram, and the at least one computer program is loaded and executed bythe processor 1001 to perform the image display method based onartificial intelligence according to the foregoing method embodiments.Certainly, the device can also have a wired or wireless networkinterface, a keyboard, an I/O interface and other components tofacilitate I/O. The device can also include other components forimplementing device functions. Details are not described herein again.

An embodiment of the present application further provides acomputer-readable storage medium, where the computer-readable storagemedium is applied to a computer device. The computer-readable storagemedium stores at least one computer program, and the at least onecomputer program is executed by the processor to perform operations ofthe image display method based on artificial intelligence performed bythe computer device according to the embodiments of the presentapplication.

What is claimed is:
 1. An image display method based on artificialintelligence, executed by a computer device, the method comprising:processing a first image to obtain a first feature image, the firstimage being an image of a local area of a smear captured by amicroscope, and the local area comprising multiple objects to be tested;obtaining a second feature image corresponding to the first featureimage, the second feature image and the first feature image having asame size; obtaining a third feature image according to an imageobtained by overlaying the first feature image and the second featureimage, a feature point in the third feature image indicating apossibility that one of the multiple objects is an abnormal object;obtaining a second image according to the third feature image, thesecond image indicating a location of an abnormal object in the firstimage; and displaying the second image superimposed on the first image.2. The method according to claim 1, wherein the obtaining a secondfeature image corresponding to the first feature image comprises:performing convolution processing on the first feature image, to obtaina fourth feature image; and performing deconvolution processing on thefourth feature image, to obtain the second feature image correspondingto the first feature image.
 3. The method according to claim 1, whereinthe image obtained by overlaying the first feature image and the secondfeature image is a fifth feature image that has the same size as thefirst feature image, and channels of the fifth feature image comprisechannels of the first feature image and channels of the second featureimage; and the third feature image is obtained by performing convolutionprocessing on the fifth feature image.
 4. The method according to claim1, wherein the third feature image is a two-channel image; and theobtaining a second image according to the third feature image comprises:obtaining a target channel image in the third feature image, wherein inthe target channel image, a feature point whose feature value is notzero indicates that a corresponding object is abnormal; for a featurepoint in the target channel image whose feature value is not zero, inresponse to that the feature value of the feature point is not less thana target threshold, adjusting the feature value of the feature point to1; in response to that the feature value of the feature point is lessthan the target threshold, adjusting the feature value of the featurepoint to 0; adjusting a size of the target channel image after adjustingthe feature values to be the same as the size of the first image; andobtaining edge feature points of an image area whose feature values are1 in the target channel image whose size is adjusted, to obtain thesecond image.
 5. The method according to claim 4, wherein after theobtaining a target channel image in the third feature image, the methodfurther comprises: removing an impurity area and an area that is lessthan a target size from the target channel image.
 6. The methodaccording to claim 1, wherein the processes of processing a first image,obtaining a second feature image, and obtaining a third feature imageaccording to an image obtained by overlaying the first feature image andthe second feature image are implemented based on an imageclassification model; and before the processing a first image to obtaina first feature image, the method further comprises: obtaining the firstimage; obtaining a pathological analysis type of the first imageaccording to an image attribute of the first image; and invoking theimage classification model corresponding to the pathological analysistype.
 7. The method according to claim 6, wherein the image attributeincludes a target magnification power used for capturing the firstimage; and the invoking the image classification model corresponding tothe pathological analysis type comprises: obtaining at least onecandidate image classification model corresponding to the pathologicalanalysis type; and in response to that a sample image magnificationpower used to train a first candidate image classification model is thesame as the target magnification power, using the first candidate imageclassification model as the image classification model.
 8. The methodaccording to claim 1, further comprising: training the imageclassification model, comprising: obtaining a training sample image, andobtaining a training target image according to the training sampleimage, wherein the training sample image is an image in which a locationof an abnormal object is marked, and the training target image is abinary image and is used to indicate the abnormal object; inputting thetraining sample image to an image classification model to be trained, toobtain a first sample feature image outputted by the imageclassification model to be trained; performing parameter adjustmentaccording to the first sample feature image and the training targetimage; and in response to that a training end condition is satisfied,using a model obtained through training as the image classificationmodel.
 9. The method according to claim 8, wherein before the inputtingthe training sample image to an image classification model to betrained, the method further comprises: obtaining a target size accordingto an image attribute of the training sample image, wherein the targetsize indicates an actual size represented by a pixel in the trainingsample image; obtaining a target scaling ratio according to the targetsize and a reference cell size, wherein the target scaling ratioindicates a ratio between the first sample feature image and thetraining sample image; and obtaining a quantity of convolution layers ofthe image classification model according to the target scaling ratio.10. The method according to claim 9, wherein the obtaining a target sizeaccording to an image attribute of the training sample image comprises:according to the image attribute of the training sample image, obtaininga sample magnification power used when the training sample image iscaptured and a pixel size that exists when the training sample image iscaptured; and obtaining the target size according to the samplemagnification power and the pixel size.
 11. An image display apparatusbased on artificial intelligence, comprising: a memory and a processorcoupled to the memory, the processor being configured to perform:processing a first image to obtain a first feature image, the firstimage being an image of a local area of a smear captured by amicroscope, and the local area comprising multiple objects to be tested;obtaining a second feature image corresponding to the first featureimage, the second feature image and the first feature image having asame size; obtain a third feature image according to an image obtainedby overlaying the first feature image and the second feature image, afeature point in the third feature image indicating a possibility thatone of the multiple objects to be tested is an abnormal object;obtaining a second image according to the third feature image, thesecond image indicating a location of an abnormal object in the firstimage; and displaying the second image superimposed on the first image.12. The apparatus according to claim 11, wherein the obtaining a secondfeature image corresponding to the first feature image comprises:performing convolution processing on the first feature image, to obtaina fourth feature image; and performing deconvolution processing on thefourth feature image, to obtain the second feature image correspondingto the first feature image.
 13. The apparatus according to claim 11,wherein the image obtained by overlaying the first feature image and thesecond feature image is a fifth feature image that has the same size asthe first feature image, and channels of the fifth feature imagecomprise channels of the first feature image and channels of the secondfeature image; and the third feature image is obtained by performingconvolution processing on the fifth feature image.
 14. The apparatusaccording to claim 11, wherein the third feature image is a two-channelimage; and the obtaining a second image according to the third featureimage comprises: obtaining a target channel image in the third featureimage, wherein in the target channel image, a feature point whosefeature value is not zero indicates that a corresponding object isabnormal; for a feature point in the target channel image whose featurevalue is not zero, in response to that the feature value of the featurepoint is not less than a target threshold, adjusting the feature valueof the feature point to 1; in response to that the feature value of thefeature point is less than the target threshold, adjusting the featurevalue of the feature point to 0; adjusting a size of the target channelimage after adjusting the feature values to be the same as the size ofthe first image; and obtaining edge feature points of an image areawhose feature values are 1 in the target channel image whose size isadjusted, to obtain the second image.
 15. The apparatus according toclaim 14, wherein after the obtaining a target channel image in thethird feature image, the method further comprises: removing an impurityarea and an area that is less than a target size from the target channelimage.
 16. The apparatus according to claim 11, wherein the processes ofprocessing a first image, obtaining a second feature image, andobtaining a third feature image according to an image obtained byoverlaying the first feature image and the second feature image areimplemented based on an image classification model; and before theprocessing a first image to obtain a first feature image, the methodfurther comprises: obtaining the first image; obtaining a pathologicalanalysis type of the first image according to an image attribute of thefirst image; and invoking the image classification model correspondingto the pathological analysis type.
 17. The apparatus according to claim16, wherein the image attribute includes a target magnification powerused for capturing the first image; and the invoking the imageclassification model corresponding to the pathological analysis typecomprises: obtaining at least one candidate image classification modelcorresponding to the pathological analysis type; and in response to thata sample image magnification power used to train a first candidate imageclassification model is the same as the target magnification power,using the first candidate image classification model as the imageclassification model.
 18. The apparatus according to claim 11, whereinthe image classification model is trained by: obtaining a trainingsample image, and obtaining a training target image according to thetraining sample image, wherein the training sample image is an image inwhich a location of an abnormal object is marked, and the trainingtarget image is a binary image and is used to indicate the abnormalobject; inputting the training sample image to an image classificationmodel to be trained, to obtain a first sample feature image outputted bythe image classification model to be trained; performing parameteradjustment according to the first sample feature image and the trainingtarget image; and in response to that a training end condition issatisfied, using a model obtained through training as the imageclassification model.
 19. The apparatus according to claim 18, whereinbefore the inputting the training sample image to an imageclassification model to be trained, the processor is further configuredto perform: obtaining a target size according to an image attribute ofthe training sample image, wherein the target size indicates an actualsize represented by a pixel in the training sample image; obtaining atarget scaling ratio according to the target size and a reference cellsize, wherein the target scaling ratio indicates a ratio between thefirst sample feature image and the training sample image; and obtaininga quantity of convolution layers of the image classification modelaccording to the target scaling ratio.
 20. An image display system basedon artificial intelligence, comprising: an objective lens configured tomagnify a local area of a smear, the local area comprising multipleobjects to be tested; an image capturing component connected to theobjective lens and configured to capture a first image through amicroscope, the first image being an image of the local area of thesmear; an image processing component connected to the image capturingcomponent and configured to: process the first image, to obtain a firstfeature image; obtain a second feature image corresponding to the firstfeature image, the second feature image and the first feature imagehaving a same size; obtain a third feature image according to an imageobtained by overlaying the first feature image and the second featureimage, a feature point in the third feature image indicating apossibility that one of the multiple objects to be tested is an abnormalobject; and obtain a second image according to the third feature image,the second image indicating a location of an abnormal object in thefirst image; an ocular lens connected to the image capturing componentand configured to display the first image; and an augmented realitycomponent connected to the image processing component and configured toproject the second image into the ocular lens, so that the ocular lensdisplays the second image superimposed on the first image.